Datasets and Implementation Details. We evaluate the AugBN method for the classification task on common adaptation datasets [1]}, [2]}, [3]}, [4]}, namely the corruption and perturbation data, CIFAR-10-C and ImageNet-C [5]}. Following previous works, we report results on the highest severity level of corruption. For more details of the datasets, please refer to Appendix . We compare the mean Classification Accuracy (mCA) over all 15 corruption types on both the datasets. We adopt the same network architecture used in the recent works  [1]}, [4]}, i.e., ResNet-26 and ResNet-50 for CIFAR-10 and ImageNet respectively. The source model for CIFAR-10 is trained on one GPU with a batch size of 64, using SGD with cosine decay scheduler and an initial learning rate of 0.01. The source model achieves 93.84% accuracy on the CIFAR-10 test set. For ImageNet, the model is trained on 8x8 TPU slices with a batch size of 8192. We use Adam optimizer along with the cosine decay scheduler with an initial learning rate of 0.1. This source model obtains 76.4% accuracy on the ImageNet validation set. For ImageNet-C and CIFAR-10-C, we report results with prior \(\lambda = 0.9\)  and \(\lambda = 0.7\)  respectively. For all classification results, we use 4 augmented samples, each composed of 2 augmentations from color distortion  [4]}, rotation, mirror reflection, Gaussian noise, vertical and horizontal flip augmentations, i.e., with \(n=4, k=2, m=6\)  (Algorithm REF ).
